In recent years, with the rapid increase of Internet use, Internet service suppliers, researchers, and administrators require a system capable of checking network conditions rapidly and accurately.
Therefore, various Internet application traffic identification and classification systems are proposed.
There are some known traffic classification systems, which include port-based classification, Deep Packet (Payload) Inspection (DPI), host-behavioral classification, and statistical approaches based on machine learning.
In the early Internet, the traffic classification largely relied on the use of transport layer port numbers. Port-based classification has been found to be less reliable since many applications hide their identity by masquerading ports and/or by using well-known ports of other application.
DPI technique looks at the packet payload to classify traffic as many applications write their signatures in the first few bytes in the payload. Given a set of unique payload signatures, DPI is more reliable and accurate. DPI is resource-intensive and futile on encrypted traffic. Additionally, DPI causes privacy and legal concerns.
Host-behavioral classifications inspect “social interaction” for classification. It shows excellent performance in identification and classification of viruses and worms that the known methods cannot easily handle, but is low in accuracy due to heuristic-based classification.
Machine learning-based method has comparatively high accuracy and a rapid execution time, but has classification and identification accuracy which depends on application traffic itself.
As described above, since the performances of the Internet application traffic identification and classification systems are limitatively evaluated, it is difficult to determine the type of the traffic classification method executable with the best performance, suitability of traffic analysis, and verification of reliability while a fair and objective evaluation reference is not provided at the time of applying each traffic classification method.